Overview

Dataset statistics

Number of variables10
Number of observations1020
Missing cells205
Missing cells (%)2.0%
Duplicate rows20
Duplicate rows (%)2.0%
Total size in memory79.8 KiB
Average record size in memory80.1 B

Variable types

Numeric6
Categorical4

Alerts

Dataset has 20 (2.0%) duplicate rowsDuplicates
salary has 52 (5.1%) missing valuesMissing
experience_years has 50 (4.9%) missing valuesMissing
city has 52 (5.1%) missing valuesMissing
education has 51 (5.0%) missing valuesMissing
id is uniformly distributedUniform
experience_years has 45 (4.4%) zerosZeros

Reproduction

Analysis started2026-02-09 05:37:46.854109
Analysis finished2026-02-09 05:37:53.298741
Duration6.44 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform 

Distinct1000
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.50098
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 KiB
2026-02-09T11:07:53.471488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1248.75
median502.5
Q3751.25
95-th percentile949.05
Maximum1000
Range999
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation289.34297
Coefficient of variation (CV)0.5781067
Kurtosis-1.2082804
Mean500.50098
Median Absolute Deviation (MAD)251.5
Skewness-0.0079721618
Sum510511
Variance83719.353
MonotonicityNot monotonic
2026-02-09T11:07:53.645749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
982
 
0.2%
1032
 
0.2%
2032
 
0.2%
802
 
0.2%
12
 
0.2%
2222
 
0.2%
632
 
0.2%
3382
 
0.2%
5032
 
0.2%
6342
 
0.2%
Other values (990)1000
98.0%
ValueCountFrequency (%)
12
0.2%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
10001
0.1%
9991
0.1%
9981
0.1%
9971
0.1%
9961
0.1%
9951
0.1%
9941
0.1%
9931
0.1%
9921
0.1%
9911
0.1%

age
Real number (ℝ)

Distinct47
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.938235
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 KiB
2026-02-09T11:07:53.778351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19.95
Q129
median42
Q352
95-th percentile62
Maximum64
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.535078
Coefficient of variation (CV)0.33062192
Kurtosis-1.1575244
Mean40.938235
Median Absolute Deviation (MAD)11
Skewness-0.040562995
Sum41757
Variance183.19834
MonotonicityNot monotonic
2026-02-09T11:07:53.976861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4334
 
3.3%
5030
 
2.9%
4530
 
2.9%
5428
 
2.7%
5228
 
2.7%
6427
 
2.6%
5626
 
2.5%
1826
 
2.5%
6226
 
2.5%
4126
 
2.5%
Other values (37)739
72.5%
ValueCountFrequency (%)
1826
2.5%
1925
2.5%
2025
2.5%
2119
1.9%
2226
2.5%
2323
2.3%
2414
1.4%
2524
2.4%
2618
1.8%
2718
1.8%
ValueCountFrequency (%)
6427
2.6%
6314
1.4%
6226
2.5%
6123
2.3%
6013
1.3%
5917
1.7%
5818
1.8%
5718
1.8%
5626
2.5%
5516
1.6%

salary
Real number (ℝ)

Missing 

Distinct945
Distinct (%)97.6%
Missing52
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean69349.509
Minimum20060
Maximum119986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 KiB
2026-02-09T11:07:54.144526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20060
5-th percentile24567.15
Q145356.25
median68453.5
Q395049.5
95-th percentile114825.45
Maximum119986
Range99926
Interquartile range (IQR)49693.25

Descriptive statistics

Standard deviation28894.841
Coefficient of variation (CV)0.4166553
Kurtosis-1.2037371
Mean69349.509
Median Absolute Deviation (MAD)25055.5
Skewness0.041559265
Sum67130325
Variance8.3491183 × 108
MonotonicityNot monotonic
2026-02-09T11:07:54.350735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
656483
 
0.3%
254082
 
0.2%
405812
 
0.2%
494862
 
0.2%
1135012
 
0.2%
383812
 
0.2%
1100932
 
0.2%
904802
 
0.2%
522652
 
0.2%
661632
 
0.2%
Other values (935)947
92.8%
(Missing)52
 
5.1%
ValueCountFrequency (%)
200601
0.1%
200771
0.1%
201261
0.1%
202351
0.1%
203381
0.1%
205261
0.1%
206611
0.1%
208141
0.1%
209221
0.1%
209561
0.1%
ValueCountFrequency (%)
1199861
0.1%
1199291
0.1%
1199111
0.1%
1198041
0.1%
1197851
0.1%
1197131
0.1%
1196161
0.1%
1196081
0.1%
1192591
0.1%
1191311
0.1%

experience_years
Real number (ℝ)

Missing  Zeros 

Distinct20
Distinct (%)2.1%
Missing50
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean9.457732
Minimum0
Maximum19
Zeros45
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size8.1 KiB
2026-02-09T11:07:54.477337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median9
Q314
95-th percentile19
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7775023
Coefficient of variation (CV)0.61087609
Kurtosis-1.2240647
Mean9.457732
Median Absolute Deviation (MAD)5
Skewness0.012383047
Sum9174
Variance33.379533
MonotonicityNot monotonic
2026-02-09T11:07:54.589769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
457
 
5.6%
1455
 
5.4%
1753
 
5.2%
353
 
5.2%
1153
 
5.2%
152
 
5.1%
952
 
5.1%
751
 
5.0%
1951
 
5.0%
1349
 
4.8%
Other values (10)444
43.5%
(Missing)50
 
4.9%
ValueCountFrequency (%)
045
4.4%
152
5.1%
247
4.6%
353
5.2%
457
5.6%
547
4.6%
643
4.2%
751
5.0%
841
4.0%
952
5.1%
ValueCountFrequency (%)
1951
5.0%
1841
4.0%
1753
5.2%
1647
4.6%
1545
4.4%
1455
5.4%
1349
4.8%
1245
4.4%
1153
5.2%
1043
4.2%

city
Categorical

Missing 

Distinct5
Distinct (%)0.5%
Missing52
Missing (%)5.1%
Memory size8.1 KiB
Bangalore
208 
Mumbai
205 
Pune
202 
Chennai
193 
Delhi
160 

Length

Max length9
Median length6
Mean length6.2613636
Min length4

Characters and Unicode

Total characters6061
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBangalore
2nd rowPune
3rd rowPune
4th rowPune
5th rowMumbai

Common Values

ValueCountFrequency (%)
Bangalore208
20.4%
Mumbai205
20.1%
Pune202
19.8%
Chennai193
18.9%
Delhi160
15.7%
(Missing)52
 
5.1%

Length

2026-02-09T11:07:54.766341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-09T11:07:54.931398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bangalore208
21.5%
mumbai205
21.2%
pune202
20.9%
chennai193
19.9%
delhi160
16.5%

Most occurring characters

ValueCountFrequency (%)
a814
13.4%
n796
13.1%
e763
12.6%
i558
9.2%
u407
 
6.7%
l368
 
6.1%
h353
 
5.8%
B208
 
3.4%
g208
 
3.4%
o208
 
3.4%
Other values (7)1378
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6061
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a814
13.4%
n796
13.1%
e763
12.6%
i558
9.2%
u407
 
6.7%
l368
 
6.1%
h353
 
5.8%
B208
 
3.4%
g208
 
3.4%
o208
 
3.4%
Other values (7)1378
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6061
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a814
13.4%
n796
13.1%
e763
12.6%
i558
9.2%
u407
 
6.7%
l368
 
6.1%
h353
 
5.8%
B208
 
3.4%
g208
 
3.4%
o208
 
3.4%
Other values (7)1378
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6061
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a814
13.4%
n796
13.1%
e763
12.6%
i558
9.2%
u407
 
6.7%
l368
 
6.1%
h353
 
5.8%
B208
 
3.4%
g208
 
3.4%
o208
 
3.4%
Other values (7)1378
22.7%

education
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing51
Missing (%)5.0%
Memory size8.1 KiB
PG
334 
High School
323 
UG
312 

Length

Max length11
Median length2
Mean length5
Min length2

Characters and Unicode

Total characters4845
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPG
2nd rowHigh School
3rd rowHigh School
4th rowUG
5th rowHigh School

Common Values

ValueCountFrequency (%)
PG334
32.7%
High School323
31.7%
UG312
30.6%
(Missing)51
 
5.0%

Length

2026-02-09T11:07:55.124848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-09T11:07:55.205886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pg334
25.9%
high323
25.0%
school323
25.0%
ug312
24.1%

Most occurring characters

ValueCountFrequency (%)
G646
13.3%
h646
13.3%
o646
13.3%
P334
6.9%
i323
6.7%
H323
6.7%
323
6.7%
g323
6.7%
S323
6.7%
c323
6.7%
Other values (2)635
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4845
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G646
13.3%
h646
13.3%
o646
13.3%
P334
6.9%
i323
6.7%
H323
6.7%
323
6.7%
g323
6.7%
S323
6.7%
c323
6.7%
Other values (2)635
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4845
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G646
13.3%
h646
13.3%
o646
13.3%
P334
6.9%
i323
6.7%
H323
6.7%
323
6.7%
g323
6.7%
S323
6.7%
c323
6.7%
Other values (2)635
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4845
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G646
13.3%
h646
13.3%
o646
13.3%
P334
6.9%
i323
6.7%
H323
6.7%
323
6.7%
g323
6.7%
S323
6.7%
c323
6.7%
Other values (2)635
13.1%

score
Real number (ℝ)

Distinct1000
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3977541
Minimum1.0002765
Maximum9.9960193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 KiB
2026-02-09T11:07:55.299617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0002765
5-th percentile1.4465294
Q13.1418167
median5.355204
Q37.5736403
95-th percentile9.4752708
Maximum9.9960193
Range8.9957429
Interquartile range (IQR)4.4318236

Descriptive statistics

Standard deviation2.5721293
Coefficient of variation (CV)0.47651842
Kurtosis-1.162684
Mean5.3977541
Median Absolute Deviation (MAD)2.2174678
Skewness0.0388106
Sum5505.7092
Variance6.615849
MonotonicityNot monotonic
2026-02-09T11:07:55.493278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6516027832
 
0.2%
7.4444497592
 
0.2%
5.121360952
 
0.2%
1.59548672
 
0.2%
9.7293387282
 
0.2%
4.4289214632
 
0.2%
1.446529362
 
0.2%
6.9554681442
 
0.2%
5.5025068172
 
0.2%
7.8062841232
 
0.2%
Other values (990)1000
98.0%
ValueCountFrequency (%)
1.000276471
0.1%
1.0058805171
0.1%
1.0100088481
0.1%
1.0376869931
0.1%
1.0574113911
0.1%
1.058179221
0.1%
1.061901661
0.1%
1.0925604631
0.1%
1.0972997531
0.1%
1.1017228291
0.1%
ValueCountFrequency (%)
9.9960193291
0.1%
9.9797445011
0.1%
9.9725447571
0.1%
9.9687367791
0.1%
9.9601201041
0.1%
9.9332361591
0.1%
9.9326315811
0.1%
9.9276647251
0.1%
9.9237949821
0.1%
9.9129907341
0.1%

review
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
Average
373 
Poor
332 
Good
315 

Length

Max length7
Median length4
Mean length5.0970588
Min length4

Characters and Unicode

Total characters5199
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowAverage
3rd rowGood
4th rowPoor
5th rowPoor

Common Values

ValueCountFrequency (%)
Average373
36.6%
Poor332
32.5%
Good315
30.9%

Length

2026-02-09T11:07:55.618023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-09T11:07:55.737939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
average373
36.6%
poor332
32.5%
good315
30.9%

Most occurring characters

ValueCountFrequency (%)
o1294
24.9%
e746
14.3%
r705
13.6%
A373
 
7.2%
v373
 
7.2%
a373
 
7.2%
g373
 
7.2%
P332
 
6.4%
G315
 
6.1%
d315
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1294
24.9%
e746
14.3%
r705
13.6%
A373
 
7.2%
v373
 
7.2%
a373
 
7.2%
g373
 
7.2%
P332
 
6.4%
G315
 
6.1%
d315
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1294
24.9%
e746
14.3%
r705
13.6%
A373
 
7.2%
v373
 
7.2%
a373
 
7.2%
g373
 
7.2%
P332
 
6.4%
G315
 
6.1%
d315
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1294
24.9%
e746
14.3%
r705
13.6%
A373
 
7.2%
v373
 
7.2%
a373
 
7.2%
g373
 
7.2%
P332
 
6.4%
G315
 
6.1%
d315
 
6.1%

department
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
IT
265 
HR
261 
Finance
255 
Sales
239 

Length

Max length7
Median length2
Mean length3.9529412
Min length2

Characters and Unicode

Total characters4032
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHR
2nd rowSales
3rd rowSales
4th rowSales
5th rowSales

Common Values

ValueCountFrequency (%)
IT265
26.0%
HR261
25.6%
Finance255
25.0%
Sales239
23.4%

Length

2026-02-09T11:07:55.872442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-09T11:07:55.959250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
it265
26.0%
hr261
25.6%
finance255
25.0%
sales239
23.4%

Most occurring characters

ValueCountFrequency (%)
n510
12.6%
a494
12.3%
e494
12.3%
T265
 
6.6%
I265
 
6.6%
H261
 
6.5%
R261
 
6.5%
F255
 
6.3%
i255
 
6.3%
c255
 
6.3%
Other values (3)717
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n510
12.6%
a494
12.3%
e494
12.3%
T265
 
6.6%
I265
 
6.6%
H261
 
6.5%
R261
 
6.5%
F255
 
6.3%
i255
 
6.3%
c255
 
6.3%
Other values (3)717
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n510
12.6%
a494
12.3%
e494
12.3%
T265
 
6.6%
I265
 
6.6%
H261
 
6.5%
R261
 
6.5%
F255
 
6.3%
i255
 
6.3%
c255
 
6.3%
Other values (3)717
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n510
12.6%
a494
12.3%
e494
12.3%
T265
 
6.6%
I265
 
6.6%
H261
 
6.5%
R261
 
6.5%
F255
 
6.3%
i255
 
6.3%
c255
 
6.3%
Other values (3)717
17.8%

working_hours
Real number (ℝ)

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.445098
Minimum4
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 KiB
2026-02-09T11:07:56.079983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q15
median7
Q39
95-th percentile11
Maximum11
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2874676
Coefficient of variation (CV)0.30724479
Kurtosis-1.2675899
Mean7.445098
Median Absolute Deviation (MAD)2
Skewness0.058193233
Sum7594
Variance5.2325078
MonotonicityNot monotonic
2026-02-09T11:07:56.219134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5156
15.3%
6131
12.8%
10127
12.5%
9126
12.4%
11123
12.1%
7121
11.9%
8120
11.8%
4116
11.4%
ValueCountFrequency (%)
4116
11.4%
5156
15.3%
6131
12.8%
7121
11.9%
8120
11.8%
9126
12.4%
10127
12.5%
11123
12.1%
ValueCountFrequency (%)
11123
12.1%
10127
12.5%
9126
12.4%
8120
11.8%
7121
11.9%
6131
12.8%
5156
15.3%
4116
11.4%

Interactions

2026-02-09T11:07:52.006966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-09T11:07:47.676631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-09T11:07:48.573488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-09T11:07:49.342410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-09T11:07:50.294941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-09T11:07:51.250438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-09T11:07:52.124464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-09T11:07:51.062183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-09T11:07:51.859320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-09T11:07:56.351028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agecitydepartmenteducationexperience_yearsidreviewsalaryscoreworking_hours
age1.0000.0000.0000.000-0.028-0.0250.023-0.027-0.0030.012
city0.0001.0000.0000.0000.0000.0290.0000.0680.0420.053
department0.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
education0.0000.0000.0001.0000.0000.0000.0530.0000.0320.038
experience_years-0.0280.0000.0000.0001.0000.0090.000-0.0640.0310.050
id-0.0250.0290.0000.0000.0091.0000.0580.0140.0110.048
review0.0230.0000.0000.0530.0000.0581.0000.0680.0430.040
salary-0.0270.0680.0000.000-0.0640.0140.0681.0000.012-0.003
score-0.0030.0420.0000.0320.0310.0110.0430.0121.0000.036
working_hours0.0120.0530.0000.0380.0500.0480.040-0.0030.0361.000

Missing values

2026-02-09T11:07:52.851716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-09T11:07:52.983536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-09T11:07:53.202455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idagesalaryexperience_yearscityeducationscorereviewdepartmentworking_hours
015625287.017.0BangalorePG9.729339GoodHR9
124654387.07.0PuneHigh School6.141344AverageSales4
233228512.014.0PuneHigh School2.287010GoodSales10
346021342.00.0PuneUG4.370934PoorSales9
4525NaN2.0MumbaiHigh School8.179590PoorSales11
563839216.07.0BangaloreHigh School4.306517GoodFinance5
6756113070.03.0ChennaiPG1.783155AverageIT8
783699767.015.0DelhiHigh School6.011064GoodSales10
8940117829.09.0DelhiPG8.606088AverageFinance8
9102846614.011.0PuneNaN8.163337GoodIT7
idagesalaryexperience_yearscityeducationscorereviewdepartmentworking_hours
101078728113623.03.0MumbaiHigh School8.579488AverageHR7
1011984152265.019.0BangaloreHigh School7.651603AverageFinance8
10128656050087.09.0PuneNaN4.198954PoorFinance9
10137221849257.015.0ChennaiPG3.523013AverageHR8
10141032849486.017.0ChennaiPG7.444450GoodSales7
10159023429078.09.0NaNHigh School2.569683PoorFinance11
101615625287.017.0BangalorePG9.729339GoodHR9
10178524346589.016.0NaNPG4.624655GoodIT5
10187422229077.017.0MumbaiHigh School8.059649AverageIT5
10196344445931.07.0BangaloreHigh School7.806284AverageHR4

Duplicate rows

Most frequently occurring

idagesalaryexperience_yearscityeducationscorereviewdepartmentworking_hours# duplicates
015625287.017.0BangalorePG9.729339GoodHR92
1631938381.014.0MumbaiUG1.446529PoorHR72
2805725408.016.0MumbaiHigh School1.595487PoorFinance102
3984152265.019.0BangaloreHigh School7.651603AverageFinance82
41032849486.017.0ChennaiPG7.444450GoodSales72
52032090480.010.0DelhiHigh School5.121361PoorSales72
622228113501.014.0MumbaiHigh School4.428921PoorHR52
733863NaN11.0BangaloreUG6.955468AverageIT102
85035422983.014.0MumbaiHigh School5.502507AverageSales102
96344445931.07.0BangaloreHigh School7.806284AverageHR42